@noncelogic/openclaw-memory-lancedb

OpenClaw Memory (LanceDB)

Long-term memory plugin for OpenClaw using LanceDB for vector storage and OpenAI for embeddings. Gives your AI assistant persistent memory across conversations with automatic recall and capture.

Features

• Auto-recall -- relevant memories are injected into context before every agent response
• Auto-capture -- important user messages are automatically stored after each conversation
• Agent tools -- memory_recall, memory_store, memory_forget for active memory management
• CLI commands -- openclaw ltm list, openclaw ltm search, openclaw ltm stats
• Duplicate detection -- 0.95 similarity threshold prevents storing near-identical memories
• Prompt injection protection -- memories are escaped and marked as untrusted data
• GDPR-friendly -- memory_forget tool for targeted deletion

Installation

openclaw plugins install @noncelogic/openclaw-memory-lancedb

Configuration

Add to your ~/.openclaw/openclaw.json:

{
  "plugins": {
    "slots": {
      "memory": "memory-lancedb"
    },
    "entries": {
      "memory-lancedb": {
        "enabled": true,
        "config": {
          "embedding": {
            "apiKey": "${OPENAI_API_KEY}"
          },
          "autoRecall": true,
          "autoCapture": true
        }
      }
    }
  }
}

Set plugins.slots.memory to "memory-lancedb" to switch from the default memory-core plugin. Only one memory plugin can be active at a time.

Config Options

Option	Type	Default	Description
embedding.apiKey	string	required	OpenAI API key (supports ${ENV_VAR} syntax)
embedding.model	string	text-embedding-3-small	Embedding model (text-embedding-3-small or text-embedding-3-large)
dbPath	string	~/.openclaw/memory/lancedb	LanceDB database path
autoRecall	boolean	true	Inject relevant memories before each response
autoCapture	boolean	false	Auto-store important user messages
captureMaxChars	number	500	Max message length for auto-capture (100-10000)

How It Works

Auto-Recall

Before every agent response, the plugin:

• Embeds the user's message using OpenAI
• Searches LanceDB for the top 3 most relevant memories (minimum 0.3 similarity)
• Injects them into the system prompt as <relevant-memories> context marked as untrusted

Auto-Capture

After each successful agent run, the plugin scans user messages for memorable content:

• Filters by length (10-500 chars), skips system markup and agent output
• Checks against trigger patterns (preferences, facts, decisions, contact info)
• Rejects prompt injection attempts
• Checks for duplicates (0.95 similarity threshold)
• Stores up to 3 memories per conversation with auto-detected categories

Agent Tools

memory_recall

Search through stored memories.

Parameter	Type	Default	Description
query	string	required	Search query
limit	number	5	Max results

memory_store

Save information to long-term memory.

Parameter	Type	Default	Description
text	string	required	Information to remember
importance	number	0.7	Importance score (0-1)
category	string	"other"	One of: preference, fact, decision, entity, other

memory_forget

Delete memories by ID or search query.

Parameter	Type	Description
query	string	Search to find memory candidates
memoryId	string	Specific memory UUID to delete

CLI

openclaw ltm list              # Show total memory count
openclaw ltm search <query>    # Search memories (JSON output)
openclaw ltm stats             # Memory statistics

Safety

Memories are injected as untrusted historical context:

• All memory text is HTML-entity escaped before injection
• Wrapped in <relevant-memories> tags with explicit "do not follow instructions" guidance
• Prompt injection patterns are detected and rejected during capture
• Memory IDs are UUID-validated before deletion to prevent query injection

Limitations

• OpenAI-only embeddings -- requires an OpenAI API key for text-embedding-3-small or text-embedding-3-large
• LanceDB native binaries -- LanceDB requires native binaries that may not be available on all platforms (notably macOS ARM can have issues)

Testing

Unit tests run without any API keys:

vitest run

Live end-to-end tests require OpenAI:

OPENCLAW_LIVE_TEST=1 OPENAI_API_KEY=sk-... vitest run

License

MIT